209results about How to "Increase loyalty" patented technology

The invention relates to the technical field of business adaptation, in particular to a business adaptation degree intelligent analysis method and system combined with a customer portrait. The method comprises the following steps: firstly, collecting customer data from a plurality of data sources, then processing the collected data, extracting interest labels of customers, constructing a customer-interest label matrix, comprehensively evaluating the purchase ability and loyalty of the customers, updating the portraits of the customers according to the interest preferences of similar customers, and then collecting the business demand data of the customers. The method comprises the following steps: constructing a business demand model, quantifying the demand intensity of a customer, generating a demand score vector, matching a customer portrait with the demand score vector, calculating an adaptation degree score, generating personalized recommendation for the customer, preferentially recommending a product or service with the highest matching degree with the customer demand, and pushing related recommendation through multiple channels. And the satisfaction degree and the conversion rate of customers are improved. According to the invention, the resource utilization efficiency and the customer satisfaction are improved, so that more efficient enterprise operation and customer service are realized.

The invention discloses a customer relationship intelligent management and analysis system based on CRM, and relates to the technical field of intelligent analysis, a business logic layer comprises a customer information management unit, a sales opportunity tracking unit and a marketing activity management unit, the customer information management unit is used for extracting basic attributes, consumption habits and preference characteristics of customers from customer data by using a data analysis technology so as to construct a comprehensive and three-dimensional customer portrait; and the customer information management unit further divides the constructed customer portraits into subdivided groups with common demands, preferences and behavior patterns through in-depth market research. According to the method, the wide customer groups are divided into the subdivided groups with common requirements, preferences and behavior patterns, so that an enterprise can more accurately understand each user group, a more targeted marketing strategy is formulated, resource allocation can be optimized to a certain extent, and the market response speed is increased.

The invention discloses a fresh commodity replenishment method and system, electronic equipment and a storage medium, and relates to the field of commodity management. According to the method, fresh food hot topics are obtained from social media, demand hotspots are determined according to the fresh food hot topics, weather influence factors are determined according to predicted weather data, and holiday influence factors and seasonal influence factors are determined according to the current date; determining sales influence factors according to the second sales data of the competitor and the first sales data of the target store; through a preset prediction model, according to the sales influence factors, the demand hotspots, the weather influence factors, the holiday influence factors and the seasonal influence factors, determining predicted demand quantities of various fresh commodities in a second preset time period after the current moment; and determining the purchase quantity according to the predicted demand quantity and the stock quantity of the target store, and determining a purchase scheme according to the purchase quantity and the supplier information. By implementing the technical scheme provided by the invention, the accuracy and efficiency of replenishment of fresh commodities are improved.

The invention belongs to the technical field of front-end development, and particularly relates to a React framework-based tree component optimization method fusing virtualization rendering and increment rendering. Comprising the steps of S1, data preprocessing; S2, dynamic virtualization rendering: dynamically calculating a node index range in a visible area according to a real-time scrolling position of a user viewport, only rendering nodes in the range, and realizing seamless lazy loading of data through scrolling event monitoring; s3, intelligent incremental updating: identifying changed nodes through a state change comparison algorithm, and executing local re-rendering only for the changed nodes based on a component life cycle control mechanism of a React framework; and S4, lazy loading cooperative control. The method aims at solving the problems in the aspects of performance, memory management, user experience and the like when a tree component in a React ecological system processes large-scale data, and is widely applied to various Web application program development scenes needing to display and operate a large-scale tree data structure.

The invention discloses a digital marketing system based on big data. The digital marketing system comprises a data acquisition module, a data analysis module, a data planning module, a customer relationship management module and an effect evaluation module. The data acquisition uses advanced technology to collect multi-source data, the analysis module deeply mines values, the planning module customizes personalized strategies, the customer relationship management operates efficiently, and the effect evaluation module monitors in real time and adjusts the strategies according to results. All the modules work cooperatively, precision and intelligence of digital marketing are achieved, the marketing effect and efficiency are improved, and a corresponding innovative marketing method and related technologies are applied to all the steps to ensure efficient operation of the system; according to the digital marketing system, comprehensive and accurate data collection and integration, deep intelligent analysis and mining of data values, personalized marketing planning execution, efficient customer relationship management and real-time dynamic effect evaluation and optimization are realized, the marketing effect and efficiency are improved, resource waste is reduced, customer loyalty is enhanced, and innovative development of enterprise marketing is promoted.

The invention discloses an intelligent travel text system based on python deep learning, and relates to the technical field of python deep learning, and the system comprises the following components: a data collection module which is used for automatically collecting comment data about a specific travel destination from various channels of various travel websites, social media platforms and online travel forums, according to the method, massive comments and evaluation data about a specific tourism destination can be automatically collected from multiple channels, the accuracy and consistency of analysis data are ensured through fine data preprocessing, fine adjustment is performed by applying an advanced deep learning sentiment analysis algorithm in combination with a specific corpus in the tourism field, and the accuracy and consistency of data analysis are ensured. The system can accurately judge the emotional tendency of tourists and deeply mine key opinions and suggestions in comments, and the deep emotional mining ability not only helps tourism management departments and scenic spots to quickly understand real feedback of the tourists, but also can accurately identify satisfaction distribution and main concerns of the tourists on services.

The invention discloses an enterprise micro-marketing auxiliary method and system based on a large language model. The efficiency and effect of marketing are improved by using the large language model. According to the method, by integrating a vector database, an embedded model and a context learning technology, the capabilities of real-time understanding of user intentions, personalized reply and quick service change adaptation are realized. The method has the core advantages of accurate user demand analysis, personalized service provision and efficient knowledge retrieval system. By analyzing user behaviors and dialogue records, product recommendation and marketing strategies according with user preferences can be provided. In addition, the method also has a real-time verbal skill updating capability, so that the timeliness and accuracy of information are ensured; the automatic order information extraction technology improves the information processing efficiency, and reduces the time and cost of manual operation. According to the invention, the marketing efficiency is improved, the interactive experience between the user and the customer service is enhanced, and competitive advantages are provided for enterprises in the market with intense competition.

The invention relates to an air purification method and system fused with AI intelligent big data analysis, and solves the problem that the purification effect is greatly reduced due to the fact that an air purifier cannot adjust operation parameters in time according to upcoming air quality changes in the operation process. The method comprises the following steps: inputting real-time gas related data and gas related data in a historical past time range into a pre-trained air quality prediction model, and outputting an air quality prediction result in a future preset time range; and the mode set by the user about the air purifier and the air quality prediction result in the future preset time range are input into the air purifier operation parameter optimization model, the optimized air purifier operation parameters are output, and the air purifier is indicated to perform corresponding operation according to the optimized air purifier operation parameters. The intelligent air purifier has the following effects that precise and intelligent air purification is achieved, the indoor air quality is effectively improved, and various requirements are met.

The invention relates to the technical field of order processing, and discloses a member order processing method, device and equipment and a storage medium, and the method comprises the steps: collecting multi-platform member information, constructing a member graph structure, and carrying out the node feature extraction, and obtaining a multi-dimensional member feature vector; performing feature fusion on the multi-dimensional member feature vector and the to-be-processed order data to obtain a member-order fusion feature vector; order classification is carried out to obtain an order classification result and a processing priority; performing multi-platform collaborative integer programming and solving to obtain a cross-platform order allocation scheme; performing member value evaluation to obtain a dynamic member value score and hierarchical division information; and inputting the dynamic member value score and the hierarchical division information into a dynamic right and interest distribution model based on the deep Q network for right and interest distribution strategy optimization to obtain a personalized member right and interest combination, and the method can comprehensively utilize multi-platform data to realize intelligent order processing and dynamic right and interest distribution.

The invention relates to the technical field of enterprise cloud management, and discloses an ERP enterprise cloud management system, which comprises a system main body deployed based on a cloud computing architecture, the system main body is integrated with a financial management module, an inventory control module, a purchase management module, a sales management module, a production plan module, a human resource management module and a customer relationship management module, and data interaction interfaces and cooperative work mechanisms are arranged among the modules; data are stored by the cloud storage unit, and a data processing task is executed by the cloud operation unit. The ERP enterprise cloud management system breaks data barriers among departments of an enterprise, integrates core modules of financial management, inventory control, purchase management, sales management, production planning, human resource management, customer relationship management and the like, ensures the accuracy and consistency of data, and realizes the cloud management of the enterprise through a data sharing function. All the modules can obtain needed data and information in real time, and seamless transmission and cooperative work of the information are achieved.

The invention relates to a construction method of a customer complaint processing system for coping with supervision complaints, which belongs to the field of customer service and supervision compliance management, and comprises the following steps: (1) establishing diversified complaint channels, and integrating the complaint channels to a unified platform; (2) complaint information is comprehensively collected and intelligently analyzed; (3) establishing a quick response and processing mechanism based on an analysis result of the step (2); (4) performing closed-loop control and continuous improvement on complaints; (5) technical support and informatization construction; and (6) personnel training and ability improvement. The method has the advantages that the processing efficiency is improved, manual intervention is reduced through the automatic processing process, and the complaint response time is shortened; (2) the processing quality is enhanced: the complaint is ensured to be professionally and timely processed by a standardized processing flow and an intelligent distribution mechanism; and (3) resource configuration optimization: tasks are intelligently allocated according to the processing capability, and waste of human resources is avoided. (4) customer satisfaction is improved, complaints are quickly responded and effectively solved, and customer credibility and loyalty are enhanced.

The invention discloses an intelligent commodity management method, and relates to the technical field of commodity management. Through the design of the whole method, the efficiency can be improved, the intelligent system can automatically manage a plurality of goods management tasks such as inventory management, purchase decision making and demand prediction, manual intervention is reduced, the management efficiency is improved, inventory optimization is facilitated, the intelligent system can accurately predict demands through data analysis and prediction algorithms, and the management efficiency is improved. The inventory level is optimized according to the prediction result, the inventory overstock or stockout phenomenon is avoided, the inventory turnover rate is improved, the cost is reduced conveniently, the intelligent system can help enterprises to optimize purchasing strategies and reduce the inventory cost, the purchasing cost is reduced through price optimization and other modes, and therefore the overall cost of the enterprises is reduced, and the enterprise experience is improved. The customer satisfaction can be improved conveniently under the overall application, and the customer satisfaction can be improved and the customer loyalty can be enhanced by meeting customer requirements in time, accurately predicting the market trend and providing personalized services and intelligent commodity management.

The invention discloses a link bandwidth allocation method and system for multilink aggregation of a multimodal mobile network. The method comprises the following steps: acquiring a mobile network base station, an equipment position and real-time environment data; an intelligent power control algorithm is adopted, and transmission power is dynamically adjusted according to environment data and network loads; acquiring real-time signal intensity data and historical data from the network equipment to the base station; predicting a minimum transmission point of signal transmission based on real-time and historical data; adjusting the direction and position of an antenna according to the position of the base station, and designing connection by adopting a beam forming antenna; setting a threshold value according to the real-time weather and the minimum transmission point data, and deploying a signal enhancer; according to the link bandwidth allocation method and system for multi-link aggregation of the multi-modal mobile network, the link quality is monitored in real time, the network is dynamically adjusted, the network load is balanced through an intelligent algorithm, the signal transmission environment is evaluated in combination with real-time weather data, the signal enhancer is deployed, and the signal coverage range is expanded.

The invention provides a large model connection context dialogue method based on an LSTM (Long Short-Term Memory) model, which belongs to the field of intelligent deep learning and comprises the steps of data preprocessing, input sequence coding, hidden state and memory cell initialization, decoding response generation, output sequence decoding, training and optimization, evaluation and adjustment. The method has high accuracy and response speed when processing a complex dialogue scene, and can be widely applied to the fields of intelligent customer service, intelligent assistants and the like.

The invention discloses a patient medical satisfaction evaluation system based on big data, and relates to the technical field of big data analysis. The medical satisfaction evaluation system comprises a data acquisition and processing module, an evaluation index management module, a satisfaction questionnaire survey module, a multi-dimensional evaluation module, a satisfaction grading module and a service quality optimization module. According to the big data-based patient doctor-seeing satisfaction degree evaluation system, through real-time collection and analysis of patient satisfaction degree data including a medical quality evaluation index and a doctor-seeing experience evaluation index, registration waiting time, diagnosis and treatment waiting time and an endemic area environment score are monitored, service bottlenecks and patient dissatisfaction fields are rapidly identified, and the patient satisfaction degree evaluation efficiency is improved. The hospital can adjust resource distribution in time, the overall quality of medical services is evaluated through the patient complaint score and the doctor-patient communication transparency score, effective communication between doctors and patients is promoted, transparency is improved, and therefore the satisfaction degree and credibility of the patients for the medical services are improved.

The invention relates to the technical field of intelligent robots, and provides an intelligent question answering method and device, electronic equipment and a storage medium, the intelligent question answering method comprises the following steps: obtaining voice information corresponding to a question asked by a user, and carrying out text conversion on the voice information to obtain a question text of the user; obtaining a user portrait model of the user, wherein the user portrait model is generated according to at least one of a mood feature, an emotion feature, a dialogue habit feature and an age feature of the user; and according to the question text and the user portrait model, querying a plurality of corresponding candidate answers, further determining a target answer from the plurality of candidate answers, performing secondary processing on the target answer to enable the processed target answer to accord with the cognitive level of the age group of the user, and outputting the target answer. The accuracy and correlation of question answering are effectively improved, and the user experience and loyalty are improved.

The invention provides an agent application method and device based on a large model, equipment and a medium, and the method comprises the steps: responding to a question asking request of a user for a first question, firstly checking whether a previous chat record exists, if yes, rewriting the first question by using the chat record, generating a more accurate second question, and then, executing the second question; judging whether the question relates to a specific target document or not, if yes, recalling a related fragment from the document, constructing a target prompt in combination with a second question, and calling a corresponding plug-in to obtain first target information and returning the first target information to the large model for processing according to a reply type of the target prompt if the reply type is function calling; if the reply is a content reply, directly generating second target information based on the target prompt and returning the second target information to the large model so as to provide a reply required by the user; according to the application, the opening and sharing of the Agent agent can be realized, so that the requirements of different service scenes are met.

The invention belongs to the technical field of enterprise management, and particularly relates to a system for evaluating the influence of enterprise digital transformation on employees, and the method comprises the steps: obtaining an enterprise digital transformation grade, and behavior data, psychological state data and work efficiency data of employees in the period of using social media under the corresponding grade; wherein the behavior data comprises the use frequency and the interaction type, and according to the enterprise digital transformation level, the employee behavior data, the psychological state data and the work performance data, determining an evaluation result of the influence of the enterprise digital transformation on the employees; according to the invention, comprehensive and scientific digital transformation influence evaluation can be provided for enterprises. By comprehensively considering multiple dimensions such as the digital transformation level, the employee social media use behavior, the psychological state and the work efficiency, the enterprise can more accurately understand the actual influence of the digital transformation on the employee level. Therefore, enterprises can find problems in time and adjust strategies, and digital transformation can be ensured to better serve development of employees and enterprises.

The invention relates to the technical field of e-commerce data management, in particular to a big data management system based on cross-border e-commerce, which comprises a data acquisition module, a multi-layer graph construction module, a multi-layer graph processing module and a multi-layer graph application module, and is characterized in that the data acquisition module collects commodity data used by users, including the users, commodities, operation behaviors and user emotions; the multi-layer graph construction module is used for forming a commodity recommendation multi-layer graph and constructing nodes and edges in emotion, behavior and time layer graphs so as to realize fine modeling of a complex relation; the multi-layer graph processing module calculates embedded vectors of all layers of graphs, and the embedded vectors are fused into aggregation embedded vectors through a full connection layer; and the multi-layer graph application module optimizes the training of a recommendation model by using an aggregation embedded vector, identifies potential interested commodities of the user by predicting a recommendation list, and particularly improves the relevance and individuation of recommendation in combination with an emotion layer graph weight, so that the problem that a recommendation system cannot timely and accurately respond to user requirements is solved, and the user experience is improved. And the user satisfaction and the platform viscosity are improved.

The invention discloses an order logistics channel allocation quotation method, which can automatically select and match a quotation table according to orders and channels by setting four charging modes and formulas of full toll, semi-toll, timber volume and cube, efficiently and accurately calculate final logistics quotation, reduce manual errors and improve the efficiency. The quotation table covers different logistics channels, service grades, destinations and weight / volume intervals, accurately matches order demands, provides diversified choices such as air transportation, sea transportation and land transportation, meets individual demands, improves customer satisfaction, and reduces logistics cost and improves operation efficiency by comparing quotations and enabling customers to choose channels with highest cost benefits. A clear quotation table and formula enhance transparency, improve customer trust, set different service levels to encourage high-quality service, guarantee customer experience and enhance loyalty.

The invention discloses an AI-based intelligent hotel guest room service evaluation method, and relates to the technical field of hotel guest room service evaluation, and the method comprises the following steps: S1, data collection: collecting guest room service feedback data of a customer through designing a questionnaire, deploying a feedback system, and integrating an application program interface (API); according to the AI-based intelligent hotel guest room service evaluation and evaluation method, a hotel can deeply understand personalized demands and preferences of each customer through a detailed scoring system and multi-dimensional data collection; according to the method, the identity and loyalty of customers to hotels can be enhanced, and the customers can feel the attention and attention of the hotels to the customers, so that the overall experience and satisfaction of the customers are improved, and repeated reservation and public praise spreading of the customers are promoted.

The invention discloses a method and a system for accurate operation of customers in a direct jurisdiction group, relates to the technical field of sales operation, and mainly solves the problems of low sales efficiency, poor customer satisfaction and high marketing cost. The method for accurate operation of customers in the direct jurisdiction group comprises the following steps: sorting and analyzing customer information; making a marketing activity strategy; determining a target customer group; the product is positioned and served; maintaining and managing customer relationships; the marketing effect is evaluated and optimized, and the direct jurisdiction group members are accurately matched for the customer group through a prediction evaluation algorithm, so that the sales conversion rate is improved; personalized recommendation and service are generated by analyzing customer data, so that customer satisfaction and loyalty are improved; and marketing advertisements and promotion activities are accurately put through a dynamic analysis algorithm, so that the marketing cost is reduced.

The invention discloses a television intelligent maintenance decision-making method, and the method comprises the following steps: obtaining historical work order information which at least comprises a television type, a fault description, a fault reason, a maintenance scheme, a maintenance category and used spare parts; constructing an intelligent fault library containing standard faults according to the historical work order information; establishing a knowledge graph according to the historical work order information and the intelligent fault database; acquiring new work order information, performing fault matching with the intelligent fault library according to fault information in the new work order information, and performing maintenance scheme recommendation according to the matched fault based on the knowledge graph; the invention also discloses a television intelligent maintenance decision-making system and a computer program. The intelligent, automatic and personalized television maintenance process can be realized, the quality and efficiency of the maintenance service can be greatly improved, the operation cost can be reduced, and the high-quality and reliable maintenance experience can be provided for the user.

The invention discloses a digital human live broadcast strategy generation method and device, computer equipment and a storage medium. According to the specific implementation scheme, the method comprises the following steps: calculating the association degree of an associated live video and a current live video in a preset time interval based on a multi-modal association degree calculation mode, and obtaining an association degree calculation result; determining a dynamic digital human model corresponding to the associated live video according to a predetermined basic digital human model and the association degree calculation result; and determining a fusion live broadcast strategy according to the basic digital human model, the personalized digital human model and the dynamic digital human model. According to the method, the fusion live broadcast strategy is determined according to the digital human models corresponding to the multi-dimensional data, dynamic changes in a live broadcast scene can be well adapted, a comprehensive live broadcast decision is made in time, and differentiation of the digital human models is further considered through multi-modal correlation degree calculation, so that the live broadcast effect is improved.

The invention discloses an advertisement pushing method and device based on artificial intelligence and a medium, and relates to the technical field of advertisement pushing, and the method comprises the steps: collecting data information of a user, carrying out the preprocessing operation, and dividing the data into a scene data set and a user interest data set; constructing a scene recognition model, and training the scene recognition model through the scene data set; constructing a user interest model, and training the user interest model through the user interest data set; analyzing the advertisement content, extracting an advertisement keyword, defining a scene keyword for the scene according to the current scene of the user, and matching and pushing the scene keyword; and carrying out effect evaluation and optimization on the pushed advertisement. According to the method, advertisements related to the current scene can be pushed to the user through scene perception advertisement pushing, so that the click rate and the conversion rate of the advertisements are improved, the satisfaction degree of the user is improved, and the loyalty of the user to the brand is enhanced.

The invention provides a customer service navigation method and device, and relates to the technical field of computers. According to the specific scheme, incoming consultation of any user is monitored, target user features corresponding to the user are obtained, and real-time consultation texts of the user are collected; taking customer service staff in an idle state at the current moment as candidate customer service staff, and obtaining target customer service staff characteristics of each candidate customer service staff; inputting the target user feature, the real-time consultation text and the target customer service feature into a trained target network model to obtain a matching degree of a user output by the target network model and each candidate customer service; and determining a target customer service corresponding to the user from the candidate customer service according to the matching degree, and distributing the real-time consultation text of the user to a customer service window corresponding to the target customer service. According to the method, the user can be matched with the target customer service most suitable for their requirements, the user is ensured to obtain the professional service most matched with their requirements, the specific requirements of the user are met, and the user experience is enhanced.

The invention discloses an enterprise product marketing management system, which comprises a real-time data analysis module, a personalized recommendation module, an integral incentive module, an operation tool integration module and a profit and loss analysis and report module, all interaction behaviors of a user on a platform are captured through a real-time data analysis module, a user portrait is created according to behavior data and preferences of the user through a personalized recommendation module by utilizing deep learning and machine learning technologies, and an integral acquisition rule and a use mode are defined through an integral incentive module. According to the invention, the marketing strategy is optimized, the attraction and incentive effect of the marketing strategy is increased, the operation tool integration module is utilized to provide a background management tool which comprises commodity management, order management and directional pushing functions, and enterprises are helped to optimize the marketing strategy and improve the operation efficiency.

The invention discloses an electric logistics vehicle charging station site selection and path optimization method in urban distribution. The method comprises the following steps: S1, determining a precondition hypothesis for constructing a semi-open multi-distribution center electric logistics vehicle path optimization model; s2, on the basis of considering the position of the charging station, constructing a semi-open multi-distribution-center electric logistics vehicle path optimization model; the semi-open multi-distribution-center electric logistics vehicle path optimization model comprises a charging station queuing model, a target function and constraint conditions; s3, improving an artificial bee colony algorithm; s4, substituting the specific parameters into an artificial bee colony algorithm to solve an optimal path at the moment; and S5, sensitivity analysis is carried out, different battery capacities and charging quotas are substituted into an artificial bee colony algorithm to solve an optimal path at the moment, and then comparison is carried out. According to the method, a semi-open multi-distribution-center electric logistics vehicle path optimization model is constructed, an improved artificial bee colony algorithm is adopted for solving, and an optimal distribution scheme under different battery capacities and charging limits is obtained.

The invention relates to the technical field of credit product recommendation, in particular to a personalized credit product recommendation system and method based on a user portrait. The method comprises the following steps: obtaining user basic information data and user behavior data, generating a user credit qualification value based on the user basic information data, and examining the credit qualification of a user based on the user credit qualification value; obtaining user basic information data and user behavior data with credit qualification, and performing user portrait construction based on the user basic information data and the user behavior data to obtain user credit intention features; and matching credit products based on the credit intention features of the user, and recommending the credit products to the user. The user experience can be improved, the credit service efficiency is improved, and the credit risk is reduced.

The invention discloses a hotel income management method based on dynamic room price optimization, and relates to the technical field of hotel income management, and the method comprises the steps: obtaining room price data, carrying out the preprocessing of the room price data, and carrying out the feature extraction; building a housing price influence factor model based on the extracted features, and predicting the housing price according to the housing price influence factor model; and determining an optimal housing price interval according to the predicted housing price, optimizing the optimal housing price interval to obtain a final housing price, and realizing hotel income management based on the final housing price. According to the method, various machine learning algorithms are adopted, the house price influence factor model is constructed, the complex nonlinear relation between the house price and various factors can be captured, and the prediction result is more adaptive. The optimal housing price interval of the future seven days is generated through dynamic prediction of the model, the housing price can be adjusted in real time, and changes of market demands are responded. According to the dynamic optimization, the flexibility and the real-time performance of income management are remarkably improved, and the hotel is helped to keep advantages in competition.